Talk to SSB using Python.
import fetcha as fetcha
import logging
# Turn off INFO-warnings
logging.getLogger().setLevel(logging.WARNING)
# >> pip install git+https://github.com/dafeda/fetcha.git --upgrade
# Instantiate object with specific table_id that refers to a SSB-table.
# 10945 refers to Monetary aggregates M1, M2 and M3:
# https://www.ssb.no/en/statbank/table/10945
ssb_10945 = fetcha.SSB("10945", language="en")# Number of rows in table.
ssb_10945.nrows_tot()
# Number of rows per period.
ssb_10945.nrows_period()
# Get all available periods
periods = ssb_10945.periods()
periods[-7:]
['2020M08', '2020M09', '2020M10', '2020M11', '2020M12', '2021M01', '2021M02']
# Fetch latest period.
# Returns a pandas dataframe with its index set with verify_integrity set to True.
# If the dataframe is lacking an index, it means that the index columns do not make up a unique combination.
df_latest = ssb_10945.fetch()
df_latest.head()
|
|
value |
| contents |
month |
|
| Monetary aggregate M1. Stocks (NOK million) |
2021M02 |
2526071.0 |
| Monetary aggregate M2. Stocks (NOK million) |
2021M02 |
2695383.0 |
| Monetary aggregate M3. Stocks (NOK million) |
2021M02 |
2697783.0 |
| Monetary aggregate M1. Transactions last 12 months (NOK million) |
2021M02 |
359029.0 |
| Monetary aggregate M2. Transactions last 12 months (NOK million) |
2021M02 |
343687.0 |
# Fetch list of periods
df_periods = ssb_10945.fetch(["2019M12", "2020M01", "2020M02"])
df_periods.head()
|
|
value |
| contents |
month |
|
| Monetary aggregate M1. Stocks (NOK million) |
2019M12 |
2159770.0 |
| 2020M01 |
2182450.0 |
| 2020M02 |
2175681.0 |
| Monetary aggregate M2. Stocks (NOK million) |
2019M12 |
2345545.0 |
| 2020M01 |
2364841.0 |
# Fetch whole year of data
df_year = ssb_10945.fetch("2020")
df_year.head()
|
|
value |
| contents |
month |
|
| Monetary aggregate M1. Stocks (NOK million) |
2020M01 |
2182450.0 |
| 2020M02 |
2175681.0 |
| 2020M03 |
2300443.0 |
| 2020M04 |
2340381.0 |
| 2020M05 |
2374607.0 |
# Fetch multiple years
df_years = ssb_10945.fetch(["2019", "2020"])
df_year.head()
|
|
value |
| contents |
month |
|
| Monetary aggregate M1. Stocks (NOK million) |
2020M01 |
2182450.0 |
| 2020M02 |
2175681.0 |
| 2020M03 |
2300443.0 |
| 2020M04 |
2340381.0 |
| 2020M05 |
2374607.0 |
# Reset index before pivoting
df_year = df_year.reset_index().pivot(index="month", columns="contents")
df_year.head()
|
value |
| contents |
Monetary aggregate M1. 12-month growth (per cent |
Monetary aggregate M1. Stocks (NOK million) |
Monetary aggregate M1. Transactions last 12 months (NOK million) |
Monetary aggregate M2. 12-month growth (per cent) |
Monetary aggregate M2. Stocks (NOK million) |
Monetary aggregate M2. Transactions last 12 months (NOK million) |
Monetary aggregate M3. 12-month growth (per cent) |
Monetary aggregate M3. Stocks (NOK million) |
Monetary aggregate M3. Transactions last 12 months (NOK million) |
| month |
|
|
|
|
|
|
|
|
|
| 2020M01 |
3.1 |
2182450.0 |
66236.0 |
3.9 |
2364841.0 |
87622.0 |
3.7 |
2368402.0 |
84912.0 |
| 2020M02 |
3.2 |
2175681.0 |
66037.0 |
3.8 |
2360484.0 |
86360.0 |
3.7 |
2364033.0 |
83138.0 |
| 2020M03 |
7.0 |
2300443.0 |
148469.0 |
7.5 |
2489403.0 |
170692.0 |
7.3 |
2492801.0 |
167960.0 |
| 2020M04 |
9.8 |
2340381.0 |
205486.0 |
9.5 |
2522315.0 |
216155.0 |
9.4 |
2525731.0 |
214558.0 |
| 2020M05 |
10.9 |
2374607.0 |
232311.0 |
10.2 |
2552508.0 |
234581.0 |
10.1 |
2555817.0 |
232003.0 |
ssb_10948 = fetcha.SSB("10948", language="en")
df_10948 = ssb_10948.fetch("2020")
|
|
|
value |
| holding sector |
contents |
month |
|
| Money holding sector |
Monetary aggregate M3. Stocks, seasonally adjusted (NOK million) |
2020M01 |
2374459.0 |
| 2020M02 |
2387955.0 |
| 2020M03 |
2499994.0 |
| 2020M04 |
2543868.0 |
| 2020M05 |
2580435.0 |
# Fetch and join
# Get another table so we have something to join with.
ssb_10948 = fetcha.SSB("10948", language="en")
df_10948 = ssb_10948.fetch("2020")
df_10948 = df_10948.reset_index().pivot_table(
index="month", columns="contents", aggfunc="mean"
)
df_10948.join(df_year).head()
|
value |
| contents |
1-month growth, seasonally adjusted (per cent) |
Monetary aggregate M3. Stocks, seasonally adjusted (NOK million) |
Transactions last month, seasonally adjusted (NOK million) |
Monetary aggregate M1. 12-month growth (per cent |
Monetary aggregate M1. Stocks (NOK million) |
Monetary aggregate M1. Transactions last 12 months (NOK million) |
Monetary aggregate M2. 12-month growth (per cent) |
Monetary aggregate M2. Stocks (NOK million) |
Monetary aggregate M2. Transactions last 12 months (NOK million) |
Monetary aggregate M3. 12-month growth (per cent) |
Monetary aggregate M3. Stocks (NOK million) |
Monetary aggregate M3. Transactions last 12 months (NOK million) |
| month |
|
|
|
|
|
|
|
|
|
|
|
|
| 2020M01 |
-10.02 |
949783.6 |
-1329.8 |
3.1 |
2182450.0 |
66236.0 |
3.9 |
2364841.0 |
87622.0 |
3.7 |
2368402.0 |
84912.0 |
| 2020M02 |
3.32 |
955182.0 |
3182.0 |
3.2 |
2175681.0 |
66037.0 |
3.8 |
2360484.0 |
86360.0 |
3.7 |
2364033.0 |
83138.0 |
| 2020M03 |
541.24 |
999997.4 |
38556.4 |
7.0 |
2300443.0 |
148469.0 |
7.5 |
2489403.0 |
170692.0 |
7.3 |
2492801.0 |
167960.0 |
| 2020M04 |
19.36 |
1017547.0 |
18928.0 |
9.8 |
2340381.0 |
205486.0 |
9.5 |
2522315.0 |
216155.0 |
9.4 |
2525731.0 |
214558.0 |
| 2020M05 |
14.82 |
1032174.2 |
17398.2 |
10.9 |
2374607.0 |
232311.0 |
10.2 |
2552508.0 |
234581.0 |
10.1 |
2555817.0 |
232003.0 |
# SSB has a limit of 300k rows per transaction.
# Some tables have more than that in one period.
ssb_10261 = fetcha.SSB("10261", language="en")# Gives warning and returns None.
df_10261 = ssb_10261.fetch()
WARNING:fetcha.ssb:Query exceeds SSB limit of 300k rows per transaction. Current query tries to fetch 607104 rows. User a filter
# Can pass filter to fetch(), but first we need to choose what we want.
# Use variable levels to see which options you have.
ssb_10261.levels
0 {'code': 'Region', 'text': 'region', 'values':...
1 {'code': 'Kjonn', 'text': 'sex', 'values': ['0...
2 {'code': 'Alder', 'text': 'age', 'values': ['9...
3 {'code': 'Diagnose3', 'text': 'diagnosis: Chap...
4 {'code': 'ContentsCode', 'text': 'contents', '...
5 {'code': 'Tid', 'text': 'year', 'values': ['20...
Name: variables, dtype: object
# We limit the region to "The whole country".
ssb_10261.levels.iloc[0]
{'code': 'Region',
'text': 'region',
'values': ['0',
'30',
'01',
'02',
'03',
'34',
'04',
'05',
'06',
'38',
'07',
'08',
'42',
'09',
'10',
'11',
'46',
'12',
'14',
'15',
'50',
'16',
'17',
'18',
'54',
'19',
'20',
'F00',
'9',
'H03',
'H04',
'H05',
'H12',
'Uoppgitt'],
'valueTexts': ['The whole country',
'Viken',
'Østfold (-2019)',
'Akershus (-2019)',
'Oslo',
'Innlandet',
'Hedmark (-2019)',
'Oppland (-2019)',
'Buskerud (-2019)',
'Vestfold og Telemark',
'Vestfold (-2019)',
'Telemark (-2019)',
'Agder',
'Aust-Agder (-2019)',
'Vest-Agder (-2019)',
'Rogaland',
'Vestland',
'Hordaland (-2019)',
'Sogn og Fjordane (-2019)',
'Møre og Romsdal',
'Trøndelag - Trööndelage',
'Sør-Trøndelag (-2017)',
'Nord-Trøndelag (-2017)',
'Nordland',
'Troms og Finnmark - Romsa ja Finnmárku',
'Troms - Romsa (-2019)',
'Finnmark - Finnmárku (-2019)',
'Total',
'Uoppgitt',
'Helseregion Vest',
'Helseregion Midt-Norge',
'Helseregion Nord',
'Helseregion Sør-Øst',
'Unknown'],
'elimination': True}
fltr = [{"code": "Region", "values": ["0"]}]df_10261 = ssb_10261.fetch(fltr=fltr)
|
|
|
|
|
|
value |
| region |
sex |
age |
diagnosis: Chapter in ICD-10 |
contents |
year |
|
| The whole country |
Males |
40-59 years |
Influenza and pneumonia |
Number of day cases |
2019 |
76.0 |
| Females |
20-39 years |
Injuries of upper extremities |
Patients with day cases |
2019 |
613.0 |
| 60-69 years |
CONGENITAL MALFORMATIONS |
Number of bed-days |
2019 |
290.0 |
| Both sexes |
20-39 years |
Cardiac dysrhythmias |
Patients with day cases |
2019 |
276.0 |
| PREGNANCY, CHILDBIRTH AND THE PUERPERIUM |
Number of out-patient consultations |
2019 |
109637.0 |
| Females |
60-69 years |
Glaucoma |
Number of day cases |
2019 |
253.0 |
| Both sexes |
Years, total |
Other maternal disorders predominantly related to pregnancy |
In-patients |
2019 |
1478.0 |
| 60-69 years |
Diabetes mellitus |
In-patients |
2019 |
488.0 |
| 70-79 years |
Other diseases of oesophagus, stomach and duodenum |
Number of bed-days |
2019 |
3265.0 |
| 0-9 years |
Malignant neoplasms of female genital organs |
Out-patients |
2019 |
1.0 |